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[Fix] Compute Kalman gain in update_iterated_dyn_share_diagonal when n > dof_Measurement #29

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[Fix] Compute Kalman gain in update_iterated_dyn_share_diagonal when n > dof_Measurement #29

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47 changes: 17 additions & 30 deletions include/IKFoM_toolkit/esekfom/esekfom.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2044,36 +2044,23 @@ class esekf{
*/

if(n > dof_Measurement)
{
std::printf("\n\n\n Too few measurement, n > dof_Measurement!!!\n\n\n");
//#ifdef USE_sparse
//Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> K_temp = h_x * P_ * h_x.transpose();
//spMt R_temp = h_v * R_ * h_v.transpose();
//K_temp += R_temp;
Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> h_x_cur = Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic>::Zero(dof_Measurement, n);
h_x_cur.topLeftCorner(dof_Measurement, 12) = h_x_;
/*
h_x_cur.col(0) = h_x_.col(0);
h_x_cur.col(1) = h_x_.col(1);
h_x_cur.col(2) = h_x_.col(2);
h_x_cur.col(3) = h_x_.col(3);
h_x_cur.col(4) = h_x_.col(4);
h_x_cur.col(5) = h_x_.col(5);
h_x_cur.col(6) = h_x_.col(6);
h_x_cur.col(7) = h_x_.col(7);
h_x_cur.col(8) = h_x_.col(8);
h_x_cur.col(9) = h_x_.col(9);
h_x_cur.col(10) = h_x_.col(10);
h_x_cur.col(11) = h_x_.col(11);
*/

// Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> K_ = P_ * h_x_cur.transpose() * (h_x_cur * P_ * h_x_cur.transpose()/R + Eigen::Matrix<double, Dynamic, Dynamic>::Identity(dof_Measurement, dof_Measurement)).inverse()/R;
// K_h = K_ * dyn_share.h;
// K_x = K_ * h_x_cur;
//#else
// K_= P_ * h_x.transpose() * (h_x * P_ * h_x.transpose() + h_v * R * h_v.transpose()).inverse();
//#endif
}
{
std::printf("\n\n\n Too few measurement, n > dof_Measurement!!!\n\n\n");
Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> H_full =
Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic>::Zero(dof_Measurement, n);
H_full.topLeftCorner(dof_Measurement, 12) = h_x_;

// Convert lazy-evaluated product and diagonal matrix to dense matrices:
Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> S =
(H_full * P_ * H_full.transpose()).eval() + dyn_share.R.asDiagonal().toDenseMatrix();

// Compute the Kalman gain:
Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> K_ =
P_ * H_full.transpose() * S.inverse();

K_h = K_ * dyn_share.h;
K_x = K_ * H_full;
}
else
{
#ifdef USE_sparse
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